In the Optimization Experiment
AnyLogic allows the use of the top-level agent root on the requirements expression and does not allow the use of the top-level agent root on the constraints.
Although they mention in the AnyLogic help that root can be used in the constraints expression, the help is wrong, root can not be used. Please check the answer to this question:
Error - can not use root. in the constraints expression - AnyLogic
So, in this case, to avoid the root error: use requirements or change your constraints, so they do not need access to root.
If I change my constraints, so they do not need access to root, I will need to reduce the number of parameters (decision variables) which I'm trying to avoid as much as possible till I discover that there is no way else.
However, I'm afraid that if I used the requirements instead of the constraints, this would reduce the optimization performance. As you know, the constraints reduce the search space (this is mentioned in the help, too), but they did not mention if the requirement does the same (although they mentioned that requirements help in guiding to the solution):
"A requirement can also be a restriction on a response that requires its value to fall within a specified range."
Does this mean that the requirement is exactly the same as the constraint (in terms of reducing the search space)?
According to the above-mentioned, if I used requirement instead of constraints (because it is not allowed to use root in the constraints expression), what is the effect on the performance?
so let's review the concepts here...
CONSTRAINTS
First, the constraints are evaluated before the simulation run, and this is only used to check if your parameters fulfill certain conditions: param1 and param2 can be evaluated between 0 and 10 but the constrain can be that the sum of both has to be below 10
This is effectively to reduce the search space since there would be no point to run the model for param1=8 and param2=8 if this is not within the search space
Requirements
Requirements are evaluated AT THE END of the simulation, that's why you can use root, so you can evaluate not only the parameters, but any variable in the simulation...
For instance if a variable ends up being above 10 when the system only allows its maximum value to be 8, then the solution is not feasible.
This means that requirements and constraints are very different, but they both find unfeasible solutions...
Other options
so from the optimization experiment side you only have these 2 options: evaluate the parameters before you run the simulation, or anything in your model after the simulation is run
Of course there's another option you can use, which is to define the restrictions inside the simulation. If your model is supposed to run for 1 day, but after 1 hour, a variable in question ends up being over 10 (which is not allowed) you can just use finishSimulation() in order to end the simulation early, and your Requirements will evaluate this variable after only 1 hour reducing the time it took to run that simulation and defining the results as unfeasible.
Conclusion
Obviously if you use requirements instead of constraints, you will have to run more simulations that you want, so the speed in which the optimization will find a solution will be much lower, so you shouldn't do that and there's no reason to do that.
Of course i have no idea what you are trying to optimize, but this is how all this works, and even though the help documentation may show an error, it wouldn't make sense to use root in the constraints.